Panel Forecasting with Asymmetric Grouping

This paper proposes an asymmetric grouping estimator for panel data forecasting. The estimator relies on the observation that the bias- variance trade-off in potentially heterogeneous panel data may be dif- ferent across individuals. Hence, the group of individuals used for parameter estimation that is optimal in terms of forecast accuracy, may be different for each individual. For a specific individual, the estimator uses cross-validation to estimate the bias-variance of all individual groupings, and uses the parameter estimates of the optimal grouping to produce the individual-specific forecast. Integer programming and screening methods deal with the combinatorial problem of a large number of individuals. A simulation study and an application to market leverage forecasts of U.S. firms demonstrate the promising performance of our new estimators